Method and apparatus for computed tomography (CT) and material decomposition with pile-up correction calibrated using real pulse pileup effect and detector response

ABSTRACT

An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). To calibrate the forward model, which represents each order of pileup using a respective pileup response matrix (PRM), an optimization search determines the elements of the PRMs that optimize an objective function measuring agreement between the spectra of recorded counts affected by pulse pileup and the estimated counts generated using forward model of pulse pileup. The spectrum of the recorded counts in the projection data is corrected using the calibrated forward model, by determining an argument value that optimizes the objective function, the argument being either a corrected X-ray spectrum or the projection lengths of a material decomposition. Images for material components of the material decomposition are then reconstructed using the corrected projection data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.15/951,329, filed Apr. 12, 2018, the entire contents of which areincorporated herein by reference.

BACKGROUND Field

Embodiments described herein relate generally to spectrally resolvedprojection data, and more specifically to correcting the projection datafor pulse pileup arising in photon-counting detectors.

Description of the Related Art

Projection data can be used for many applications, including: computedtomography, radiography, mammography, and tomosynthess. Projection datareveals the internal structure of an object by transmitting radiationthrough the object and detecting the effect of the object on thetransmitted radiation by comparing transmitted radiation with the objectpresent in the beam path versus when the object is absent. In absorptionimaging the projection data represents Radon transforms of theattenuation along the rays traced by the radiation. Computed tomography(CT) uses projection data acquired for a series of projection angles togenerate a sinogram from which an image of the internal structure of theobject can be reconstructed. For example, a reconstruction algorithm,such as filtered back-projection or an iterative reconstruction method,can be used to approximate an inverse Radon transform to reconstruct avolumetric image from the series of projection images acquired atdifferent projection angles.

CT imaging systems and methods are widely used for medical imaging anddiagnosis. Typically an X-ray source is mounted on a gantry thatrevolves about a long axis of the body. An array of X-ray detectorelements are mounted on the gantry, opposite the X-ray source.Cross-sectional images of the body are obtained by taking projectiveattenuation measurements at a series of gantry rotation angles, andprocessing the resultant projection data using a CT reconstructionalgorithm.

Some CT scanners use energy-integrating detectors to measure CTprojection data. Alternatively, photon-counting detectors (PCDs) havebeen developed using a semiconductor such as cadmium zinc telluride(CZT) capable of converting X-rays to photoelectrons to quickly anddirectly detect individual X-rays and their energies, which isadvantageous for spectral CT. To obtain spectrally resolved projectiondata, the PCDs divide the X-ray beam into spectral bins (also calledenergy components) and count a number of photons in each of the bins.Many clinical applications can benefit from spectral CT technology,e.g., due to better material differentiation and improved beam hardeningcorrection.

One advantage of spectral CT, and spectral X-ray imaging in general, isthat materials having atoms with different atomic number Z also havedifferent spectral profiles for attenuation. Thus, by measuring theattenuation at multiple X-ray energies, materials can be distinguishedbased on the spectral absorption profile of the constituent atoms (i.e.,the effective Z of the material). Distinguishing materials in thismanner enables a mapping from the spectral domain to the materialdomain, which is referred to as material decomposition.

Material decomposition of spectral CT data is possible because theattenuation of X-rays in biological materials is dominated by twophysical processes—photoelectric and Compton scattering. Thus, theattenuation coefficient as a function of energy can be approximated bythe decompositionμ(E,x,y)=μ_(PE)(E,x,y)+μ_(C)(E,x,y),wherein μ_(PE)(E,x,y) is the photoelectric attenuation and μ_(C)(E,x,y)is the Compton attenuation. This decomposition of the attenuationcoefficient can be rearranged instead into a decomposition into twomaterial components, with material 1 being a high-Z material such asbone and material 2 being a low-Z material such as water. Accordingly,the attenuation decomposition can be expressed asμ(E,x,y)≈μ₁(E)c ₁(x,y)+μ₂(E)c ₂(x,y),wherein c_(1,2)(x,y) is a spatial function describing the concentrationsof material 1 and material 2 located at position (x, y). The order ofimage reconstruction and material decomposition can be interchanged.When material decomposition is performed before image reconstruction,the spectral resolved attenuation at the pixels is resolved intoprojection lengths for the materials, such that the total attenuation ata photon-counting detector (PCD) due to the i^(th) material componentsis the product of the projection length, L_(i), and the attenuationcoefficient of the i^(th) material component, μ_(i), at a predefineddensity.

While semiconductor-based PCDs provide unique advantages for spectralCT, they also create unique challenges. For example, without correctingfor nonlinearities and spectral shifts in the detector response, imagesreconstructed from semiconductor-based PCDs can have poorer imagequality. The detector response corrections can include corrections forpileup, ballistic deficit effects, polar effects, characteristic X-rayescape, and space-charge effects. The combination of detector responsecorrection and material decomposition creates a complex problem.Accordingly, computationally efficient methods are desired to correctfor the spectral and nonlinear detector response of PCDs to ensurehigh-quality reconstructed images.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of this disclosure is provided byreference to the following detailed description when considered inconnection with the accompanying drawings, wherein:

FIG. 1A shows examples of pulse trains illustrating no pileup,first-order pileup, and second-order pileup, and the signals generatedfor these pulse trains by an ideal detector (ideal events) and anon-ideal detector (observed pulse train), also shown are the recordedcounts and energies for the non-ideal detector at the end of therespective detection windows, according to one implementation;

FIG. 1B shows a plot of an example of X-ray spectra for a true X-rayspectrum and a measured/recorded X-ray spectrum, illustrating adistortion due to pileup in non-ideal photon counting detectors,according to one implementation;

FIG. 2 shows a flow diagram of a method to correct projection data usinga forward model of pileup, and then reconstruct a computed tomography(CT) image using the corrected projection data, according to oneimplementation;

FIG. 3 shows the respective dimensions of pileup response matrices(PRMs) of a pileup response function (PRF), according to oneimplementation;

FIG. 4A shows an example of a method to correct measured X-ray spectrumusing a continuous-to-continuous analytic pileup model, according to oneimplementation;

FIG. 4B shows an example of a method to correct measured X-ray spectrumusing a discrete-to-discrete parametric pileup model, according to oneimplementation;

FIG. 5A shows an image of attenuation for a cross-section of a phantom;

FIG. 5B shows a reconstructed image of the attenuation of the phantomthat was reconstructed from projection data representing an idealdetector, according to one implementation;

FIG. 5C shows a reconstructed image of the attenuation of the phantomthat was reconstructed from a non-ideal detector that is corrected forthe detector response but not for pileup, according to oneimplementation;

FIG. 5D shows a reconstructed image of the attenuation of the phantomthat was reconstructed from a non-ideal detector that is corrected bothfor the detector response and for pileup, according to oneimplementation;

FIG. 6A shows an image of a water component of material decomposition ofthe attenuation of the phantom;

FIG. 6B shows an image of a bone component of the phantom;

FIG. 7A shows an image of a water component reconstructed after materialdecomposition of the projection data corresponding to FIG. 5C, which iscorrected for the detector response but not for pileup, according to oneimplementation;

FIG. 7B shows an image of a bone component reconstructed after materialdecomposition of the projection data corresponding to FIG. 5C, accordingto one implementation;

FIG. 8A shows an image of a water component reconstructed after materialdecomposition of the projection data corresponding to FIG. 5D, which iscorrected both for the detector response and for pileup, according toone implementation;

FIG. 8B shows an image of a bone component reconstructed after materialdecomposition of the projection data corresponding to FIG. 5D, accordingto one implementation; and

FIG. 9 shows a schematic of an implementation of a computed tomography(CT) scanner, according to one implementation.

DETAILED DESCRIPTION

Photon-counting-detector based CT system (PCCT) have many advantages,including: spectrally resolving X-ray radiation, high-spatialresolution, and low electronic noise. At high X-ray flux rates, however,photon-counting detectors can suffer from pile up. That is, due to thecount rate limitation of existing ASIC and semiconductor-detectortechnology, the measured count can deviate from the true count when theincident flux is sufficiently high that multiple X-ray photons arefrequently incident on respective detector elements within the detectiontime window.

The pileup effect is illustrated in FIGS. 1A and 1B, which show that infirst-order and higher-order pile up the recorded/measured spectrum canbecome shifted relative to the actual spectrum of X-rays incident on thedetector elements as a result of overlap and interactions between X-raypulses/signals within given detector element during a detection timewindow. For example, in an ideal nonparalyzable (NP) detector (“idealevents” in FIG. 1A), the effects at high count rates due to multiplephotons arriving during the detection window manifests as the multiplephotons being counted as one X-ray photon having the highest energydetected during the detection window. Thus, even for this “ideal” case,the output count no longer accurately reflects the true counts, and thedetected spectrum is distorted relative to the true spectrum. Thiseffect is known as pulse pile up.

Moreover, for non-ideal detectors, physical effects due, e.g., to chargeinteractions between photo-electrons, depletion, saturation effects,etc. can cause further distortions of the measured spectrum relative tothe true spectrum, as illustrated in the “observed pulse train” and“recorded count/energy” examples shown in FIG. 1A. Thus, the practicaleffect of pulse pile up is to shift of the recorded/measured energyspectrum relative to the true energy spectrum, as shown in FIG. 1B.

If left uncorrected, the detector response determines the distributionof actually energy deposited in photon counting detector, and aninaccurate response model will induce errors to the photon energydistributions, causing inaccurate decomposition results. Severalstrategies can counteract or otherwise minimize the effects of pileup.For example, making the area of detector elements smaller pushes theflux threshold higher before pileup becomes an issue, but the pileupeffect still must be corrected for when this higher flux threshold isexceeded. When pixel size decreases and the flux rate per area is heldconstant, the count rate per pixel decreases proportionally to thedecrease in detector area. Thus, small pixel design mitigates the pileupproblem to a degree, but is not a complete solution. Moreover,decreasing the pixel size present other challenges, such as increasingcharge sharing effects, which, like pile up, also causes spectraldistortion and degrades the image quality and performance of the imagingsystem.

Additionally, an analytical model can be used to estimate and thencorrect for the pile-up effect. However, analytical models are limitedby that fact that they are based on the analytical pulse shape an idealNP detector, which is illustrated in FIG. 1A, and is not representativeof the real detector response, leading to the model mismatch that couldaffect the accuracy of subsequent processing steps such as materialdecomposition and image reconstruction. Further, using analytical modelsfor spectrum correction and then material decomposition would requirethe evaluation of a complete pileup response function, which is resourceintensive both in terms of storage and computation. For example,precomputing and storing an analytical model would demand a lot ofstorage space and hence would not be practical, in particular, when thefull pileup response function varies from element-to-element of thedetector array. Alternatively, calculating the pileup response functionon-the-fly is also not currently practical because of the length of timethese computations would require.

Using heuristic models such as a neural network to correct for thedetector response also have their drawbacks. For example, these methodsrequire a large set of training data to estimate the model parameters.But even with a large set of training data, the training does notguarantee a global minimizer because the objective function used fortraining is nonconvex, posing an obstacle to maintaining steadibilityand robustness when correcting for pileup using a neural network. Also,the use the neural network method for the pileup correction withrealistic detector is an under-developed field that remains poorlyunderstood.

To address the above-discussed challenges presented by the pileup effectin non-ideal detectors, the methods described herein apply a parametricpile-up model accounting for the real detector response, as describedbelow. In certain implementations, the methods described herein use alow-dimensional parametric pile-up forward model based on real detectorresponse for PCCT. Further, the methods described herein can include amaterial decomposition method that is integrated with the forward modelfor pileup correction. For example, the parametric pile-up model can berepresented using a sequence of low dimensional pile-up matricescorresponding to respective orders of pileup, and the dimensionality ofthe pile-up matrix grows with the order of pileup, as described below.Further, the model parameters of the pile-up model can be directlyestimated from a set of calibration scans acquired using the realdetector to be calibrated. Advantageously, this estimation of the modelparameters is convex, and, hence, guarantees a global minimizer thatallows for optimal performance. In certain implementations, the methodsdescribed herein also incorporate material decomposition together withthe estimated pile-up model to recover from the measured projectiondata, which has a distorted spectrum, corrected projection datarepresenting the true un-distorted spectrum of the X-rays incident onand detected at the detector. Alternatively, when the pileup correctionis integrated with material decomposition, as discussed below, themethods described herein can directly generate path lengths for thebasis-materials of the material decomposition.

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views, themethods described herein can be better appreciated by considering thenon-limiting flow diagram of method 100, shown in FIG. 2. FIG. 2 shows aflow diagram of method 100, which is an overall workflow of a PCCTpileup correction, material decomposition, and image reconstructionmethod. The above discussed improvements and advantages of the methodsdescribed herein are variously included in implementations of processes110 and 120, which are directed to generating a forward modelrepresenting the real pileup effect and detector response measured inthe calibration data 106 to correct for pileup; and (ii) performing amaterial decomposition. That is, in spectral CT using photon-countingdetectors (PCDs), an image reconstruction process 130 is preceded bypreprocessing steps including correcting for the detector response andmaterial decomposition.

FIG. 1 shows a flow diagram of method 100 for reconstructing an image ofan object OBJ based on a series of projection measurements of the objectOBJ performed at different projection directions (i.e., computedtomography (CT) using projective measurements). The data processing isperformed using two inputs—calibration values 106 and projection data104. The projection data have multiple spectral components, making itcompatible with material decomposition based on the different spectralabsorption characteristics of high-Z and low-Z materials. In addition tobeing applicable to CT applications as illustrated by the non-limitingexample in FIG. 2, processes 110 and 120 are also applicable to non-CTapplications involving projective measurements, including radiography,mammography, and tomosynthesis, which are within the scope of theapparatuses and methods described herein and do not depart from thespirit of this disclosure, as would be appreciated by a person ofordinary skill in the art.

Process 110 of the image reconstruction method 100 corrects theprojection data for the real detector response, including pileup. Thiscan include using various calibrations 106 to precompute a feed forwardmodel.

Next, the method 100 proceeds to process 120, in which the spectrallyresolved projection data is corrected to account for pulse pileup usingthe feed forward model, and various other calibrations can be applied tocorrect the projection data (e.g., denoising, background subtraction,corrections for nonlinear-detector response, etc.). The corrections canbe applied prior to, after, or in conjunction with the decomposition ofthe spectral components into material components, while still in theprojection domain (i.e., before image reconstruction).

Although images of the object OBJ can be reconstructed from the spectralcomponents of the projection data and then material decomposition isperformed in the image domain on these spectral-component images withoutdeparting from the spirit of the disclosure, this alternative order ofthe processing steps will not be described in the non-limiting exampleillustrated in FIG. 2.

After process 120, the method 100 proceeds to process 130 whereinmultiple images are reconstructed using an image reconstruction process(e.g., an inverse Radon transformation). The image reconstruction can beperformed using a back-projection method, a filtered back-projection, aFourier-transform-based image reconstruction method, an iterative imagereconstruction method (e.g., algebraic reconstruction technique or thelike), a matrix-inversion image reconstruction method, or a statisticalimage reconstruction method. For non-CT applications (e.g., radiography,mammography, and tomosynthesis), process 130 is omitted, and the non-CTapplication can proceed directly from process 120 to either process 140or process ISO.

After process 130, the method 100 proceeds to process 140 whereinpost-processing steps are performed on the data, including: volumerendering, smoothing, densoing, filtering, and various methods forcombining the material images to convey physical concept (e.g., maps ofthe attenuation, density, or effective Z density).

Finally, in step 150 of method 100 the image is presented to a user. Theimage presentation can be performed by displaying the image on a digitalscreen (e.g., LCD monitor), by printing the image on a suitable medium(e.g., paper or an X-ray film), or by storing the image on acomputer-readable medium.

The discussion herein is focused primarily on process 110 and process120. As discussed above, these processes are applicable to both CT andnon-CT applications, including: radiography, mammography, andtomosynthesis, which are within the applications of the methodsdescribed herein, as would be understood by a person of ordinary skillin the art.

In summary, according to a nonlimiting implementation, method 100includes, at process 110, generating a lower dimension pulse pileupmodel/parameters in forward model, which is precomputed from calibrationdata 106 with real detector response and the stored in a non-transitorycomputer readable medium of a CT apparatus. Further, method 100includes, at process 120, applying material decomposition, either afteror together with the application of the precomputed pileup model, toprojection data 104 from photon-counting detectors (PCDs) to compute thepath length of different material from real PCCT measurements. Atprocess 130, method 100 includes reconstructing material images from thematerial-component path lengths/sinograms generated at process 120. Thereconstruction method used in process 130 can be any known method,including analytical reconstruction methods and iteration reconstructionmethods. At process 140, method 100 includes post-processing such asartifact reduction techniques that are applied to further improve imagequality.

The discussion below focuses primarily on process 110 and process 120.As discussed above, these processes are applicable to both CT and non-CTapplications, including: radiography, mammography, and tomosynthesis,which are within the spirit of the disclosure, as would be understood bya person of ordinary skill in the art.

Returning to process 110, the projection data correction can berepresented by the recorded/measured energy S_(out)(E) derived from theenergy spectrum of X-rays incident on the detector S_(in)(E), wherein animplementation of the detector response function is given byS _(out)(E)=ne ^(−nt) ∫dE ₀ R ₀(E,E ₀)S _(in)(E ₀)+n ² e ^(−nt) ∫∫dE ₀dE ₁ R ₁(E,E ₀ ,E ₁)S _(in)(E ₀)S _(in)(E ₁)|higher orderwherein R₀ is the linear response function, R₁ is the quadratic responsefunction representing first-order pileup, and τ is the dead time of thedetector. Each of R₀, R₁, and τ can depend on the detector element andthe incident angle of the X-ray radiation. Additionally, therecorded/measured energy S_(out)(E) also depends on higher-order terms,including second-order pileup, etc. The incident spectrum is given byS _(in)(E _(i))=S _(air)(E)exp[−μ₁(E)L ₁−μ₂(E)L ₂],wherein μ₁ and μ₂ are the attenuation coefficients of the basismaterials for the material decomposition, L₁ and L₂ are the projectionlengths and S_(air) is the X-ray radiation in the absence of attenuationdue to an imaged object OBJ (i.e., when μ₁=μ₂=0).

The number of counts in a given energy bin is calculated by theexpressionN _(k) =ΔT∫dE w _(k)(E)S _(in)(E),wherein ΔT is the integration time and w_(k)(E) is the spectral functionof the k^(th) energy bin of the photon counting detectors. For example,the spectral function could be a square function, which is defined as

${w_{k}(E)} = \{ {\begin{matrix}1 & {W_{k} < E < W_{k + 1}} \\0 & {otherwise}\end{matrix}.} $

The discretization of the detected energy spectrum into energy binsenables the simplification of the feed-forward model. Accordingly, thegeneral forward model of PCCT detector with the pileup effect can bedescribed as:

${{S_{out}(E)} = {{\lambda_{M} \cdot \Delta}\;{T \cdot {\sum\limits_{m = 0}^{\infty}{{P( { E \middle| m ,S_{i\; n}} )}{\Pr(m)}}}}}},$wherein S_(out)(E) is the output spectrum, λ_(M) is the measured countrate, ΔT is the scan time, P(E|m,S_(in)) is the mth-order pileupspectrum, Pr(m) is the probability of having the m order pileup (i.e.,it is determined from Poisson statistics). Further, the mth-order pileupspectrum can be expressed by:P(E|m,S _(in))=∫ . . . ∫Pr(E|E ₀ , . . . E _(m))S _(in)(E ₀) . . . S_(in)(E _(m))dE ₀ . . . dE _(m)wherein S_(in)(E_(i)) represents the input energy spectrum energy,Pr(E|E₀, . . . , E_(m)) is a conditional probability density functionthat the recorded/measured energy will be E given that the energies them+1 X-rays incident on the detector element in the time window ΔT are{E₀, E₁, . . . , E_(m)}. Since Pr(E|E₀, . . . , E_(m)) is determined bydetector response for pulse pileup, and is called the pileup responsefunction (PRF). Using the methods described below to estimate the PRF,pileup correction can be performed to account for real physical effectsand detector properties. The methods described below are advantageousbecause they overcome the significant obstacles resulting from the factsthat (i) directly modeling PRF using continuous function is verychallenging and (ii) the dimension of PRF increases exponentially as afunction of the pileup order. To overcome these significant obstacles,the methods described herein approximates the continuous PRF functionusing a small-scale pileup response matrix (PRM) estimated fromcalibration data 106. By this approximation, the forward modelsimplifies to a sequence of small matrices, which effectivelycoarse-grain the PRF at a manageable level of detail.

FIG. 3 illustrates the exponential growth of the PRF with respect to thepileup order, and the simplification provided by the methods describedherein. Consider for example, the case in which PRM represent acontinuous spectrum model for a 120 kVp X-ray source with a resolutionof 1 kVp. FIG. 3A shows that the PRM for no-pileup would have 120×120elements, corresponding to a two-dimensional matrix with indices for Eand E₀ having a total of 14,400 elements. Similarly, the PRMrepresenting first-order pileup includes 1,728,000 elements (i.e.,120×120×120 elements), and over 20 billion elements for the PRMrepresenting second-order pileup (i.e., 120×120×120×120=20,7360,000elements), illustrating that, due to exponential growth, evensecond-order pileup could present a data storage challenge.

However, in material decomposition procedure, only 2 unknowns (i.e.,material components) are decomposed from the spectral component,suggesting that the energy can be resolved at a coarser resolutionrequiring fewer than 120 spectral components. Thus, recovering thecontinuous spectrum might not be necessary since the informationprovided by each of the 120 components is not unique (e.g., below theK-edge the attenuation is primarily due to only twoprocesses—photoelectric absorption and Compton scattering—and all uniqueinformation can be conveyed in the absence of noise using only twospectral components). Therefore, to reduce the computation and storagecosts, the continuous spectrum is coarse-grained by discretizing thespectra using only a few energy bins, dramatically reducing the size ofthe PRMs. It is noted that, even when the spectrum dimension uses only afew energy bins (i.e., more than two), the material decomposition isstill an over-determined problem, including beneficial redundancy. Thenumber of energy bins can be, e.g., 3, 4, 5, 6, 8, and 10, or othervalue as would be understood by a person of ordinary skill in the art.

FIGS. 4A and 4B compare a continuous method 500 with a discrete (i.e.,coarse-grained) method 540 of correcting pileup using respective pileupmodels. In FIG. 4A, a continuous-to-continuous analytic pileup model isapplied at step 520 to map a continuous measured spectrum 510(illustrated as the plot to the right of spectrum 510) to generate acontinuous corrected spectrum 530 (illustrated as in the plot to theright of spectrum 530). As a comparison, FIG. 4B illustrates adiscrete-to-discrete parametric pileup model being applied at step 560to a discrete measured spectrum 550 (illustrated as the plot to theright of spectrum 540) to generate a discrete corrected spectrum 570(illustrated as the plot to the right of spectrum 570). FIG. 4B shows anon-limiting example of the number of detected energy bins being sevenand the number of corrected energy bins being nine. In certainimplementations, the number of detected energy bins can equal the numberof corrected energy bins. The span of the center energies of thediscrete energy bins can be adjusted, e.g., based on empirical factorsto optimize and improve image quality.

The simplification from a continuous-to-continuous analytic pileup modelto a discrete-to-discrete parametric pileup model is described next. Asdiscussed above, in the continuous domain the mth order pileup spectrumcan be expressed byP(E|m,S _(in))=∫ . . . |dE ₀ . . . dE _(m) Pr(E|E ₀ , . . . E _(m))S_(in)(E ₀) . . . S _(in)(E _(m)).When the detected signals is translated into counts of the A energy binN_(k), the above expression simplifies toP(k|m)=∫ . . . ∫dE ₀ . . . dE _(m)∫_(W) _(k) ^(W) ^(k+1) Pr(E|E ₀ , . .. E _(m))S _(in)(E ₀) . . . S _(in)(E _(m))dENext, basis functions can be introduced to represent the discretizationof the energy bins corresponding to the various orders of pileup. Thesespectral basis functions can also be a square functions, defined as

${u_{l}(E)} = \{ {\begin{matrix}1 & {U_{l} < E < U_{l + 1}} \\0 & {otherwise}\end{matrix},} $and the count rate in l^(th) energy bin is given by the expressionX _(l) =∫dE _(m) u _(l)(E _(m))S _(in)(E _(m)),The energy spectrum S_(in)(E_(m)) can then be approximated asΣ_(l)μ_(l)(E_(m)), which in turn allows the PRM to be approximated andfurther simplified as

${P( k \middle| m )} = {{\int_{W_{k}}^{W_{k + 1}}{{dE}{\int\mspace{14mu}{\ldots\mspace{14mu}{\int{{dE}_{0}\mspace{14mu}\ldots\mspace{14mu}{dE}_{m}{\Pr( { E \middle| E_{0} ,\ldots\mspace{14mu},E_{m}} )}\ {\sum\limits_{l}{X_{l,0}{u_{l}( E_{0} )}\mspace{14mu}\ldots\mspace{14mu}{\sum\limits_{q}{X_{q,m}{u_{q}( E_{m} )}}}}}}}}}}} = {{\sum\limits_{l}\mspace{14mu}{\ldots\mspace{14mu}{\sum\limits_{q}{X_{l} \times \ldots \times X_{q}{\int_{W_{k}}^{W_{k + 1}}{{dE}{\int_{U_{l}}^{U_{l + 1}}{{dE}_{0}\mspace{14mu}\ldots\mspace{14mu}{\int_{U_{q}}^{U_{q + 1}}{{dE}_{m}{\Pr( { E \middle| E_{0} ,\ldots\mspace{14mu},E_{m}} )}}}}}}}}}}} = {\sum\limits_{l}\mspace{14mu}{\ldots\mspace{14mu}{\sum\limits_{q}{X_{l} \times \ldots \times X_{q}{P_{k,l,\mspace{11mu}\ldots\mspace{11mu},q}^{(m)}.}}}}}}}$wherein, P_(k, l, . . . q) ^((m)) are matrix elements of an m+2dimensional matrix P^((m))∈K_(out)×K_(in) ^(m+1), K_(out) is the numberof energy bins of the corrected spectrum, and K_(in) is the number ofenergy bins of the measured/recorded spectrum. With the aboveapproximation, the PRM for mth-order response has simplified toestimating the K_(in) unknown parameters X_(l) and calculating thematrix elements P_(k, l, . . . q) ^((m)).

Consequently, the forward model formula can be expressed as

${\overset{¯}{y} = {{\lambda_{M} \cdot \Delta}\;{T \cdot {\sum\limits_{m = 0}^{\infty}{\alpha_{m}P_{m}\overset{\overset{m + {1\mspace{14mu}{terms}}}{︷}}{( {x \otimes \ldots \otimes x} )}}}}}},$wherein y is a vector in which the kth element of the vector representsthe measured mean count N_(k) of the kth energy bin, α_(m) is aparameter representing the combination of coefficients of the mth-orderpileup (e.g., α_(m) includes Pr(m), which is the probability for mX-rays being within the detection window as determined based on thePoisson statistics, and possibly other calibration factors), P^((m)) isthe PRM defined above for pileup of order m, x is a vector of the truemean counts within respective energy bins (i.e., corrected for pileup).The symbol ‘⊗’ is the tensor product (also known as the outer product orKronecker product). To estimate the matrix elements of the PRMs, theforward model can be recast into

${\overset{¯}{y} = {{{\lambda_{M} \cdot \Delta}\;{T \cdot {\sum\limits_{m = 0}^{\infty}{\alpha_{m}X_{m}p_{m}}}}} = {{\lambda_{M} \cdot \Delta}\;{T \cdot \overset{\_}{X}}\; p}}},$wherein the elements of P_(m) have been reshaped from a matrix into thevector p_(m), and X is a matrix generated using calibration data forα_(m) together with X_(m)=I_(K) _(out) ⊗(x⊗ . . . ⊗x)^(T). Whereas inthe previous forward model formula the PRMs were treated as known andthe vector x was treated as the argument, in this recast forward modelformula, the vector x (i.e., X_(m)) is treated as known and the argumentis the vector p_(m) (i.e., the elements of P_(m)). Using this recastformulation, it is possible to estimate the PRMs, even without having anactual pulse-height analysis (PHA) associated with the ASIC. Given thatthe counts have a Poisson distribution, the PRMs can be estimated fromthe calibration data 106 by solving for the argument p that optimizesthe maximum likelihood estimation (MLE):

${\hat{p} = {\arg\;{\max\limits_{p \geq 0}\{ {\sum\limits_{i}\{ {{- {\sum\limits_{k = 1}^{K_{out}}{{\overset{¯}{y}}_{ik}(p)}}} + {\sum\limits_{k = 1}^{K_{out}}{y_{ik}{\log( {{\overset{¯}{y}}_{ik}(p)} )}}}} \}} \}}}},$wherein y _(ik)(p) is the mean count rate at energy bin measurementi,y_(ik) is the measurement count rate with pileup effect (i.e., themeasured/recorded counts in the projection data uncorrected for pileup).Using the above MLE, a unique solution is ensured for {circumflex over(p)}. The calibration data can include projection measurements in theabsence of the object OBJ using various values to the current andvoltage settings of the X-ray source of the X-ray imager (e.g., CTscanner).

Process 120 is now discussed in more detail. Having estimated the PRMsfrom the calibration data 106 as discussed above, the forward model isthen ready to be used to determine a corrected spectrum (i.e., the meancounts before pileup x) from the recorded spectrum (i., therecorded/measured counts y). The corrected spectrum can be generated,for example, by optimizing an objective function (also referred to as acost function). For example, the above objective function (i.e., thePoisson likelihood MLE) can be used, except the argument being optimizedis the counts before pileup, x, rather than the elements of the PRMs.That is, the forward model formula can be expressed as

${\overset{¯}{y} = {{\lambda_{M} \cdot \Delta}\;{T \cdot {\sum\limits_{m = 0}^{\infty}{\alpha_{m}P_{m}\overset{\overset{m + {1\mspace{14mu}{terms}}}{︷}}{( {x \otimes \ldots \otimes x} )}}}}}},$and the corrected spectrum can be generated by solving the optimizationproblem

$\hat{x} = {\arg\;{\max\limits_{x \geq 0}{\{ {\sum\limits_{i}\{ {{- {\sum\limits_{k = 1}^{K_{out}}{{\overset{¯}{y}}_{ik}(p)}}} + {\sum\limits_{k = 1}^{K_{out}}{y_{ik}{\log( {{\overset{¯}{y}}_{ik}(x)} )}}}} \}} \}.}}}$Having solved for the corrected count x, material decomposition can thenbe performed using the corrected count x.

In certain implementations, the spectrum correction and the materialdecomposition can be integrated into a single step. For example, thecorrected counts are given by

x_(i) = Δ T∫_(U_(i))^(U_(t + 1))dES_(i n)(E),and the incident spectrum is given byS _(in)(E)=S _(air)(E)exp[−μ₁(E)L ₁−μ₂(E)L ₂].Thus, by approximating the attenuation coefficient of a given materialcomponent n={1,2} as μ_(n)(E)=μ _(n) ^((i))+Δ_(n) ^((i))(E), wherein μ_(n) ^((i)) is an average attenuation within the i^(th) energy bin dueto a predefined density of the n^(th) material component, the correctedcount x_(i) can be expressed in terms of projection lengths L={L₁,L₂} ofthe material components, as given by the expression

${x_{i} = {\Delta T{\exp( {{- {\overset{¯}{\mu}}_{1}^{(i)}}L_{1}} )}{\exp( {{- {\overset{¯}{\mu}}_{2}^{(i)}}L_{2}} )}{\int_{U_{i}}^{U_{i + 1}}{{{dES}_{air}(E)}{\exp\lbrack {{{- \mspace{2mu}\Delta}\;{\mu_{1}(E)}L_{1}} - {\Delta\;{\mu_{2}(E)}L_{2}}} \rbrack}}}}},$which, prior to beam hardening corrections, can be approximated asx _(i)≈exp(−μ ₁ ^((i)) L ₁)exp(−μ ₂ ^((i)) L ₂)x _(i) ^((air)),wherein x_(i) ^((air))∫_(U) ₁ ^(U) ^(i+1) dE S_(air)(E). Accordingly, bysubstituting the above expression into the above optimization, theobjective function can be recast to directly solve for the projectionlengths L={L₁,L₂} such that the material decomposition is integratedwith the pileup correction. For example the optimization problem can beformulated as

$\begin{matrix}{\hat{L} = {\arg\;{\max\limits_{L \geq 0}\{ {\sum\limits_{i}\{ {{- {\sum\limits_{k = 1}^{K_{out}}{{\overset{¯}{y}}_{ik}( {x(L)} )}}} + {\sum\limits_{k = 1}^{K_{out}}{y_{ik}{\log( {{\overset{¯}{y}}_{ik}( {x(L)} )} )}}}} \}} \}}}} \\{= {\arg\;{\max\limits_{L \geq 0}{\{ {\sum\limits_{i}\{ {{- {\sum\limits_{k = 1}^{K_{out}}{{\overset{¯}{y}}_{ik}(L)}}} + {\sum\limits_{k = 1}^{K_{out}}{y_{ik}{\log( {{\overset{¯}{y}}_{ik}(L)} )}}}} \}} \}.}}}}\end{matrix}$So far, the objective functions mentioned have included only a datafidelity term subject to the constraint that the argument be positive.Additionally, the objective function can include additional constraintsand regularization terms.

In general, any known method can be used to iteratively converge to theargument optimizing the objective function. For example, objectivefunction can be optimized using an optimization search such as asteepest descent method, a gradient-based method, a genetic algorithm, asimulated annealing method, or other known method of searching for anargument that optimizes the objective function. Further, the argumentused in the optimization search can be the spectrally resolved countrates or the projection lengths of material components.

To illustrate the use of different types of objective functions, thenon-limiting example is used in which the projection lengths are theargument being used to optimize the objective function. That is,determining the optimal projection lengths L={L₁,L₂} by optimizing theobjective function φ(L₁,L₂) is illustrated. This objective functioncombines the measured projection data y_(m) with correspondingcalculated values y _(m) obtained from the detector model (i.e., forwardmodel) discussed previously.

Several different objective functions φ(L₁,L₂) are possible. In oneimplementation, the objective function is the least squares of thedifference between the measured counts y_(m) and the calculated countsfrom the forward model y _(m), i.e.,

${\varphi( {L_{1},L_{2}} )} = {\sum\limits_{m}{( {y_{m} - {\overset{¯}{y}}_{m}} )^{2}.}}$

In one implementation, the objective function is the weighted leastsquares of the difference between the measured counts y_(m) and thecalculated counts y _(m), i.e.,

${{\varphi( {L_{1},L_{2}} )} = {\sum\limits_{m}\frac{( {y_{m} - {\overset{¯}{y}}_{m}} )^{2}}{\sigma_{m}^{2}}}},$where σ_(m) is a measure of the measurement uncertainty of the m^(th)energy bin of detector.

In one implementation, the objective function is the Poisson likelihoodfunction, i.e.,

${\varphi( {L_{1},L_{2}} )} = {\sum\limits_{m}{\lbrack {{y_{m}{\log( {\overset{¯}{y}}_{m} )}} - {\overset{¯}{y}}_{m}} \rbrack.}}$

Process 120 can also include various other calibrations and correctionsto the corrected counts and/or projection lengths, including beamhardening corrections, k-escape corrections, polar effect corrections,etc. as described in U.S. patent application Ser. No. 14/676,594 andU.S. patent application Ser. No. 14/593,818, which are both incorporatedherein in their entirety.

FIGS. 5A, 5B, 5C, and 5D show representative results for imagesreconstructed with and without using method 100. FIG. 5A shows a phantomused to simulate real and ideal detector responses. FIG. 5B shows areconstructed image generated using an ideal detector unaffected bypileup, whereas FIGS. 5C and 5D show reconstructed images using a realdetector response that includes pileup. In FIG. 5C, the projection datawas not corrected for pileup but was corrected for the real detectorresponse, the corrected projection data was then used to reconstruct thedisplayed attenuation image. In FIG. 5D, the projection data wascorrected for both pileup and the real detector response, the correctedprojection data was then used to reconstruct the displayed attenuationimage. FIG. 5D illustrates that significant improvements to the imagequality are realized by correcting for both pileup and the real detectorresponse.

Similarly. FIGS. 6A and 6B show the phantom from FIG. 5A decomposed intomaterial components corresponding to water and bone, respectively. FIGS.7A and 7B show a material decomposition into water and bone,respectively, using projection data that was not corrected for pileupbut was corrected for the real detector response. FIGS. 8A and 8B show amaterial decomposition into water and bone, respectively, usingprojection data that was corrected for both pileup and the real detectorresponse. Comparing FIGS. 8A and 8B with their counterparts in FIGS. 7Aand 7B, the improvements provided by the pileup corrections describedherein are once again clearly evident.

FIG. 9 shows a computed tomography (CT) scanner 900 having bothenergy-integrating detectors arranged in a third-generation geometry andPCDs arranged in a fourth-generation geometry. Illustrated in FIG. 9 isan implementation for placing the PCDs in a predeterminedfourth-generation geometry in combination with a detector unit 903 in apredetermined third-generation geometry in a CT scanner system. Thediagram illustrates relative positions among the X-ray source 912, thecollimator/filter 914, the X-ray detector 903, and the photon-countingdetectors PCD1 through PCDN. The projection data 104 can be obtainedusing the CT scanner 900, and the projection data 104 can be obtainedusing the CT scanner 900 in which the detector unit 903 is omitted.

In addition to the configuration of the X-ray source 912 and thedetectors including the detector unit 903 and the PCDS show in FIG. 9,other types and combinations of X-ray detectors and X-ray source can beused to obtain the projection data. For example, either the detectorunit 903 or the PCDS could be omitted from the scanner shown in FIG. 9and the scanner could still obtain projection data, albeit differentfrom the projection data obtained using the complete system shown inFIG. 9. Further, kV switching could be used with energy-integratingdetectors or PCDs. In certain implementations, the PCDS can be directX-ray detectors using semiconductors to convert the X-rays directly tophotoelectrons without first generating scintillation photons.Additionally, in certain implementations, a broadband X-ray source canbe used with spectrally-resolving X-ray detectors. Thesespectrally-resolving X-ray detectors can include PCDs in anyconfigurations (e.g., a predetermined third-generation geometry or apredetermined fourth-generation geometry) or energy-integratingdetectors preceded by respective spectral filters. In certainimplementations, the X-ray source can include multiple narrow-band X-raysources, such as in a dual source CT scanner. In general, any knowncombination of detector type and configuration together with any knowntype or combination of X-ray sources can be used to generate theprojection data.

Returning to FIG. 9, FIG. 9 also shows circuitry and hardware foracquiring, storing, processing, and distributing X-ray projection data.The circuitry and hardware include: a processor 970, a networkcontroller 980, a memory 978, and a data acquisition system 976.

In one alternative implementation, the CT scanner includes PCDs but doesnot include the energy-integrating detector unit 903.

As the X-ray source 912 and the detector unit 903 are housed in a gantry940 and rotate around circular paths 910 and 930 respectively, thephoton-counting detectors PCDs and the detector unit 903 respectivelydetects the transmitted X-ray radiation during data acquisition. Thephoton-counting detectors PCD1 through PCDN intermittently detect theX-ray radiation that has been transmitted and individually output acount value representing a number of photons, for each of thepredetermined energy bins. On the other hand, the detector elements inthe detector unit 903 continuously detect the X-ray radiation that hasbeen transmitted and output the detected signals as the detector unit903 rotates. In one implementation, the detector unit 903 has denselyplaced energy-integrating detectors in predetermined channel and segmentdirections on the detector unit surface.

In one implementation, the X-ray source 912, the PCDs and the detectorunit 903 collectively form three predetermined circular paths thatdiffer in radius. At least one X-ray source 912 rotates along a firstcircular path 910 while the photon-counting detectors are sparselyplaced along a second circular path 920. Further, the detector unit 903travels along a third circular path 930. The first circular path 910,second circular path 920, and third circular path 930 can be determinedby annular rings that are rotatably mounted to the gantry 940.

Additionally, alternative embodiments can be used for placing thephoton-counting detectors in a predetermined fourth-generation geometryin combination with the detector unit in a predeterminedthird-generation geometry in the CT scanner.

In one implementation, the X-ray source 912 is optionally a singleenergy source. In another implementation, the X-ray source 912 isconfigured to perform a k-switching function for emitting X-rayradiation at a predetermined high-level energy and at a predeterminedlow-level energy. In still another alternative embodiment, the X-raysource 912 is a single source emitting a broad spectrum of X-rayenergies. In still another embodiment, the X-ray source 912 includesmultiple X-ray emitters with each emitter being spatially and spectrallydistinct.

The detector unit 903 can use energy integrating detectors such asscintillation elements with photo-multiplier tubes or avalanchephoto-diodes to detect the resultant scintillation photons fromscintillation events resulting from the X-ray radiation interacting withthe scintillator elements. The scintillator elements can be crystalline,an organic liquid, a plastic, or other know scintillator.

The PCDs can use a direct X-ray radiation detectors based onsemiconductors, such as cadmium telluride (CdTe), cadmium zinc telluride(CZT), silicon (Si), mercuric iodide (HgI₂), and gallium arsenide(GaAs).

The CT scanner also includes a data channel that routes projectionmeasurement results from the photon-counting detectors and the detectorunit 903 to a data acquisition system 976, a processor 970, memory 978,network controller 980. The data acquisition system 976 controls theacquisition, digitization, and routing of projection data from thedetectors. The data acquisition system 976 also includes radiographycontrol circuitry to control the rotation of the annular rotating frames910 and 930. In one implementation data acquisition system 976 will alsocontrol the movement of the bed 916, the operation of the X-ray source912, and the operation of the X-ray detectors 903. The data acquisitionsystem 976 can be a centralized system or alternatively it can be adistributed system. In an implementation, the data acquisition system976 is integrated with the processor 970. The processor 970 performsfunctions including reconstructing images from the projection data,pre-reconstruction processing of the projection data, andpost-reconstruction processing of the image data. The processor 970 alsoperforms the functions and methods described herein.

The pre-reconstruction processing of the projection data can includecorrecting for detector calibrations, detector nonlinearities, polareffects, noise balancing, and material decomposition. Additionally, thepre-reconstruction processing can include preforming various steps ofmethod 100, including processes 110 and 120.

Post-reconstruction processing can include filtering and smoothing theimage, volume rendering processing, and image difference processing asneeded. For example, the post-reconstruction processing can be performedusing various steps of method 100 (e.g., process 140)

The image-reconstruction process can be performed using filteredback-projection, iterative-image-reconstruction methods, orstochastic-image-reconstruction methods.

Additionally, the image-reconstruction processing can include a combinedprocess of reconstructing and denoising the reconstructed images usingvarious steps of method 100 (e.g., process 130).

Both the processor 970 and the data acquisition system 976 can make useof the memory 976 to store, e.g., the projection data 104, reconstructedimages, the calibration data 106, various other parameters, and computerprograms.

The processor 970 can include a CPU that can be implemented as discretelogic gates, as an Application Specific Integrated Circuit (ASIC), aField Programmable Gate Array (FPGA) or other Complex Programmable LogicDevice (CPLD). An FPGA or CPLD implementation may be coded in VHDL,Verilog, or any other hardware description language and the code may bestored in an electronic memory directly within the FPGA or CPLD, or as aseparate electronic memory. Further, the memory may be non-volatile,such as ROM, EPROM, EEPROM or FLASH memory. The memory can also bevolatile, such as static or dynamic RAM, and a processor, such as amicrocontroller or microprocessor, may be provided to manage theelectronic memory as well as the interaction between the FPGA or CPLDand the memory.

Alternatively, the CPU in the reconstruction processor may execute acomputer program including a set of computer-readable instructions thatperform the functions described herein, the program being stored in anyof the above-described non-transitory electronic memories and/or a harddisk drive, CD, DVD, FLASH drive or any other known storage media.Further, the computer-readable instructions may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with a processor, such asa Xenon processor from Intel of America or an Opteron processor from AMDof America and an operating system, such as Microsoft VISTA, UNIX,Solaris, LINUX, Apple, MAC-OS and other operating systems known to thoseskilled in the art. Further, CPU can be implemented as multipleprocessors cooperatively working in parallel to perform theinstructions.

In one implementation, the reconstructed images can be displayed on adisplay. The display can be an LCD display, CRT display, plasma display,OLED, LED or any other display known in the art.

The memory 978 can be a hard disk drive, CD-ROM drive, DVD drive, FLASHdrive, RAM, ROM or any other electronic storage known in the art.

The network controller 980, such as an Intel Ethernet PRO networkinterface card from Intel Corporation of America, can interface betweenthe various parts of the CT scanner. Additionally, the networkcontroller 980 can also interface with an external network. As can beappreciated, the external network can be a public network, such as theInternet, or a private network such as an LAN or WAN network, or anycombination thereof and can also include PSTN or ISDN sub-networks. Theexternal network can also be wired, such as an Ethernet network, or canbe wireless such as a cellular network including EDGE, 3G and 4Gwireless cellular systems. The wireless network can also be WiFi,Bluetooth, or any other wireless form of communication that is known.

While certain implementations have been described, these implementationshave been presented by way of example only, and are not intended tolimit the teachings of this disclosure. Indeed, the novel methods,apparatuses and systems described herein may be embodied in a variety ofother forms; furthermore, various omissions, substitutions and changesin the form of the methods, apparatuses and systems described herein maybe made without departing from the spirit of this disclosure.

The invention claimed is:
 1. A computed tomography (CT) scanner,comprising: a gantry including a rotating member configured to rotateabout an opening configured to accommodate an object being imaged, anX-ray source fixed to the rotating member and configured to radiateX-rays towards the opening of the gantry; an X-ray detector comprising aplurality of photon-counting detector elements, the X-ray detectorconfigured to detect the X-rays transmitted from the X-ray source afterhaving been transmitted through the opening in the gantry, which isconfigured to accommodate an object being imaged, the X-ray detectorgenerating projection data comprising recorded counts detected atrespective photon-counting detector elements comprising the X-raydetector, and the recorded counts (i) correspond to energy bins of theX-ray detector, (ii) are affected by pileup of X-rays within a detectiontime window, and (iii) have a pileup-affected spectrum that is distortedrelative to a true spectrum of the detected X-rays; and processingcircuitry configured to obtain a forward model of pulse pileup thatestimates the pileup-affected spectrum of the recorded counts based onan input of a pileup-corrected spectrum proposed to represent the truespectrum of the detected X-rays, the forward model including parametersspecific to the respective photon-counting detector elements, and theforward model representing two or more orders of pulse pileup andincluding a respective pileup response matrix for each one of the two ormore orders of pulse pileup, and correct the projection data using theforward model to generate corrected projection data, wherein annth-order of pulse pileup of the two or more orders of pulse pileuprepresents n+1 X-rays being detected at one of the photon-countingdetector elements within a detection time window, and n is a positiveinteger.
 2. The CT scanner according to claim 1, wherein the processingcircuitry is further configured to correct the projection data byoptimizing a value of an objective function to determine an optimalvalue of an argument of the objective function, the objective functionrepresenting agreement between the pileup-affected spectrum of theobtained projection data and the estimated pileup-affected spectrumgenerated using the forward model.
 3. The CT scanner according to claim2, wherein the processing circuitry is further configured to obtain theforward model, wherein the argument of the objective function, which isbeing used to optimize the objective function, is one of the input ofthe pileup-corrected spectrum in the forward model and projectionlengths of a material decomposition, which are used to determine theinput of the pileup-corrected spectrum in the forward model, wherein,when the argument is the projection lengths, the correcting of theprojection data using the forward model to generate the correctedprojection data is performed such that the corrected projection dataincludes the projection lengths, and the processing circuitry is furtherconfigured to reconstruct material-component images using the projectionlengths of the corrected projection data, and, when the argument is thespectrum of X-rays detected at the one of the photon-counting detectorelements, the correcting of the projection data using the forward modelto generate the corrected projection data is performed such that thecorrected projection data includes corrected counts corresponding toother energy bins, and the processing circuitry is further configured todecompose the corrected counts into projection lengths of the materialdecomposition and reconstruct material-component images using theprojection lengths.
 4. The CT scanner according to claim 2, wherein theprocessing circuitry is further configured to correct the projectiondata using the objective function that is one of a Poisson likelihoodfunction, a least-squares difference function, and a weightedleast-squares difference function.
 5. The CT scanner according to claim1, wherein the processing circuitry is further configured to obtain theforward model, wherein, in the forward model, the input spectrum of thedetected X-rays is determined using a multiplication between anattenuation coefficient of a material component and a projection lengthfor the material component, for each material component of a materialdecomposition.
 6. The CT scanner according to claim 1, wherein theprocessing circuitry is further configured to obtain the forward model,wherein, in forward model, the spectrum of the recorded counts ispartitioned according to the energy bins of the X-ray detector, and theinput spectrum representing the detected X-rays is partitioned accordingto other energy bins having one or more different partitions than theenergy bins of the X-ray detector.
 7. The CT scanner according to claim1, wherein the processing circuitry is further configured to obtain theforward model, wherein, in the forward model for each one of thephoton-counting detector elements, each nth-order of pulse pileup of thetwo or more orders of pulse pileup is scaled by a Poisson-distributedprobability of n+1 X-rays being detected at the one of thephoton-counting detector elements within the detection time window. 8.The CT scanner according to claim 1, wherein the processing circuitry isfurther configured to decompose the corrected projection data intomaterial components to generate a material decomposition, andreconstruct material-component images of the object being imaged usingthe material decomposition.
 9. The CT scanner according to claim 1,wherein the processing circuitry is further configured to correct theprojection data using the forward model to generate the correctedprojection data, wherein the correcting of the projection data using theforward model integrates a material decomposition with correcting thepileup-affected spectrum of the recorded counts of the obtainedprojection data to generate the corrected projection data, whichincludes projection lengths of the material decomposition, and theprocessing circuitry is further configured to reconstructmaterial-component images of the object being imaged using the correctedprojection data.
 10. The CT scanner according to claim 1, wherein theprocessing circuitry is further configured to generate the forward modelusing calibration data representing projection data generated by theX-ray detector in an absence of the object being imaged, the parametersspecific to the respective photon-counting detector elements beingdetermined as an argument that optimizes an objective function thatrepresents agreement between the pileup-affected spectrum of theobtained projection data and the estimated pileup-affected spectrumgenerated using the forward model, the argument of the objectivefunction being the parameters specific to the respective photon-countingdetector elements, store, in a non-transitory computer readable medium,the parameters specific to the respective photon-counting detectorelements that optimize the objective function, and perform the obtainingof the forward model by reading, from the non-transitory computerreadable medium, the stored parameters specific to the respectivephoton-counting detector elements.
 11. The CT scanner according to claim1, wherein the processing circuitry is further configured to obtain theforward model, wherein, in forward model, a number of the energy bins ofthe X-ray detector is equal to a number of the other energy bins, whichis less than seven.
 12. The CT scanner according to claim 1, wherein theprocessing circuitry is further configured to obtain the projectiondata, wherein the photon-counting detector elements are semiconductordetectors comprising one or more of cadmium telluride (CdTe), cadmiumzinc telluride (CZT), silicon (Si), mercuric iodide (HgI2), and galliumarsenide (GaAs).